Identification of Pathogenic Genes of Pterygium Based on the Gene Expression Omnibus Database

Overview

Journal Int J Ophthalmol

Specialty Ophthalmology

Date 2019 Apr 27

PMID 31024802

Citations 10

Authors

Xiao-Li Yue

Zi-Qing Gao

Affiliations

Soon will be listed here.

Abstract

Aim: To identify the pathogenic genes in pterygium.

Methods: We obtained mRNA expression profiles from the Gene Expression Omnibus database (GEO) to identify differentially expressed genes (DEGs) between pterygium tissues and normal conjunctiva tissues. The Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway analysis, protein-protein interaction (PPI) network and transcription factors (TFs)-target gene regulatory network was performed to understand the function of DEGs. The expression of selected DEGs were validated by the quantitative real-time polymerase chain reaction (qRT-PCR).

Results: A total of 557 DEGs were identified between pterygium and normal individual. In PPI network, several genes were with high degrees such as and and were the most downstream genes regulated by TFs. qRT-PCR results showed that and were significantly up-regulated in pterygium and and were also up-regulated with no significance, which was consistent with the our integrated analysis.

Conclusion: The deregulated genes might be involved in the pathology of pterygium and could be used as treatment targets for pterygium.

Citing Articles

Assessing genetic and environmental components for pterygium: a nationwide study in Taiwan.

Lee J, Chen W, Lin K, Tsai L, Kuo C, See L Sci Rep. 2024; 14(1):18464.

PMID: 39122774 PMC: 11315885. DOI: 10.1038/s41598-024-69482-5.

Advances in artificial intelligence applications for ocular surface diseases diagnosis.

Ji Y, Liu S, Hong X, Lu Y, Wu X, Li K Front Cell Dev Biol. 2023; 10:1107689.

PMID: 36605721 PMC: 9808405. DOI: 10.3389/fcell.2022.1107689.

Identification of the circRNA-miRNA-mRNA Regulatory Network in Pterygium-Associated Conjunctival Epithelium.

Yu J, Luo J, Li P, Chen X, Zhang G, Guan H Biomed Res Int. 2022; 2022:2673890.

PMID: 36398070 PMC: 9666032. DOI: 10.1155/2022/2673890.

A Novel System for Measuring Pterygium's Progress Using Deep Learning.

Wan C, Shao Y, Wang C, Jing J, Yang W Front Med (Lausanne). 2022; 9:819971.

PMID: 35237630 PMC: 8882585. DOI: 10.3389/fmed.2022.819971.

Transcriptome Analysis of Pterygium and Pinguecula Reveals Evidence of Genomic Instability Associated with Chronic Inflammation.

Suarez M, Echenique J, Lopez J, Medina E, Iros M, Serra H Int J Mol Sci. 2021; 22(21).

PMID: 34769520 PMC: 8584501. DOI: 10.3390/ijms222112090.

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